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An Adaptive Multi-Features Aware Correlation Filter for Visual Tracking
Zhang XY(张祥越)1,2,3,4,5; Ding QH(丁庆海)6; Luo HB(罗海波)1,4,5; Hui B(惠斌)1,4,5; Chang Z(常铮)1,4,5
Department光电信息技术研究室
Source PublicationIEEE ACCESS
ISSN2169-3536
2019
Volume7Pages:134772-134781
Indexed BySCI ; EI
EI Accession number20200408062098
WOS IDWOS:000498671000001
Contribution Rank1
Funding OrganizationKey Project of Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences [Y6TB020401]
KeywordVisual tracking correlation filter adaptive multi-features fusion computer vision
Abstract

In recent years, correlation filter (CF) based tracking methods have attracted more attention due to its low computational complexity and excellent performance. Most CF based tracking methods adopt CNN features of multiple layers to train the tracker for better performance. These methods fuse CNN features of multiple layers directly, and cannot make full use of the valuable information contained in the CNN features. In this paper, an adaptive multi-features aware correlation filter method is proposed. By extracting several basic features, different combinations of CNN features are formed. The proposed method can select an optimal feature combination for tracking adaptively according to the object appearance at the current frame. Experimental results show that the proposed method can track different challenging sequences robustly. By evaluating on the OTB-100 dataset, it can be found that the proposed method is advantageous compared with the state-of-the-art methods.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25982
Collection光电信息技术研究室
Corresponding AuthorZhang XY(张祥越)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
5.Key Laboratory of Image Understanding and Computer Vision, Chinese Academy of Sciences, Shenyang 110016, China
6.Space Star Technology Company Ltd., Beijing 100086, China
Recommended Citation
GB/T 7714
Zhang XY,Ding QH,Luo HB,et al. An Adaptive Multi-Features Aware Correlation Filter for Visual Tracking[J]. IEEE ACCESS,2019,7:134772-134781.
APA Zhang XY,Ding QH,Luo HB,Hui B,&Chang Z.(2019).An Adaptive Multi-Features Aware Correlation Filter for Visual Tracking.IEEE ACCESS,7,134772-134781.
MLA Zhang XY,et al."An Adaptive Multi-Features Aware Correlation Filter for Visual Tracking".IEEE ACCESS 7(2019):134772-134781.
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